A Tabu-GA-based parallel machine scheduling with restrained tool resources

Abstract

In flexible manufacturing systems, disordered parallel machine scheduling may cause interruptions in the production line; thus, a reasonable scheduling plan benefits profits of manufacturing. In the literature, the operation sequence, the operation assignment, the tool scheduling, and the tool switching problems have been studied with unlimited resources. However, in practical manufacturing process, a scheduling problem along with these problems with restrained resources should be studied to make scheduling solutions more performable. There are a number of operations processed on a group of parallel machines, and each operation requires a set of tools where the tool copy number and tool service life are limited. The objective of this problem is to minimize the make-span with restrained resources. This article studies a parallel machine scheduling problem combining operation scheduling, tool scheduling and restrained resources, which benefit the real industry. A Tabu-Genetic Algorithm is proposed to find the optimal solution. In small size problem, the proposed method can achieve similar results whereas the mixed integer programming approach can obtain the exact solution. However, in large size problem, the latter cannot obtain the solutions within acceptable times, whereas the proposed method can find solutions within reasonable times. This algorithm performs better when compared to Tabu search algorithm and genetic algorithm in selecting local and global optima.

Metaheuristics for optimizing unrelated parallel machines scheduling with unreliable resources to minimize makespan

“…Constraints (11 and 12) confirm that the starting time and completion time of the dummy jobs have to respect the preventive maintenance period [t Bi , t Ei ]. Finally, constraints (13)(14)(15)(16)(17) define the non-negativity and integrality of the variables.…”

“…A two-factor analysis of variance is used to study the effect of the number of jobs and the number of machines on the average relative percentage deviation RPD of the algorithms. Average relative percentage deviations were obtained for the eight different jobs sets (10,12,15,20,50,70,100,150) and four different machines sets (2,3,5,7), each with five replications. Table 9 summarizes these results.…”

“…Several studies, 5,10–18 have been proposed to solve parallel machine scheduling using different metaheuristic algorithms to minimize different objective functions such as makespan, total tardiness, the weighted sum of completion times, and the number of tardy jobs. Computational studies were carried out to determine the heuristics’ performance and execution time.…”

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

“…In a study by , the total nonproductive time of the machining process, including tool travelling time, tool switching time and Z-axis compensation moving time is minimised by presenting a hybrid variable neighbourhood search/tabu search and neighbourhood generation strategy. Wang, Zou, and Wang (2020) found the optimal solutions to a parallel machine scheduling problem combining operation scheduling, tool scheduling and restrained resources are obtained by a Tabu-Genetic algorithm. Dang et al (2021) proposed a mathematical model and combination of a genetic algorithm and an integer linear programming formulation to solve industry-size instances of parallel machine scheduling with tool replacements.…”

Queuing theory-based optimization research on the multi-objective transportation problem of fourth party logistics

“…In terms of model solving, the genetic algorithm (GA) is a random search method with the advantages of multi-directional optimization. The convergence results of GA have a slight correlation with the initial value, so GA is widely used in scheduling, 17,18 process parameter optimization, 19 path optimization, 20 etc. Chen et al 21 used GA to solve 4plrp with 10 nodes.…”